Multi-Robot Inverse Reinforcement Learning under Occlusion with Interactions

Abstract

We consider the problem of learning the behavior of multiple mo-bile robots executing fixed trajectories in a common space and pos-sibly interacting with each other in their execution. The mobile robots are observed by a subject robot from a vantage point from which it can observe a portion of their trajectories only. This prob-lem exhibits wide-ranging applications and the specific application we consider here is that of the subject robot who desires to pene-trate a simple perimeter patrol by two interacting robots and reach a goal location. Our approach extends single-agent inverse rein-forcement learning (IRL) to a multi-robot setting and partial ob-servability, and models the interaction between the mobile robots as equilibrium behavior. IRL provides weights over the features of the robots ’ reward functions, thereby allowing us to learn their preferences. Subsequently, we derive a Markov decision process based policy for each other robot. We extend a predominant IRL technique and empirically evaluate its performance in our applica-tion setting. We show that our approach in the application setting results in significant improvement in the subject’s ability to predict the patroller positions at different points in time with a correspond-ing increase in its successful penetration rate.